Effective Altruism: Research Priorities and Opportunities

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Preamble

Some key links and references

Reinstein CV

• Barriers to effective giving project

• Guide: Researching and writing for Economics students

• Alternateves to academic journals proposal

---

EA principles, some key hubs

• EA Forum

• CEA guiding principles

• [Curated List of EA Orgs](https://eaforum.issarice.com/posts/f6kg8T2Lp6rDqxWwG/list-of-ea-related-organisations"

• 80000hours.org

---

Grants, fellowships, jobs

• Future of Humanity Institute vacancies

• Rethink priorities is hiring

• GPI/Forethought fellowship, [pre-doctoral](https://globalprioritiesinstitute.org/wp-content/uploads/GPI-Predoctoral-Research-Fellow-Economics.pdf

• EA Funds grants, e.g,, for “Promising research into animal advocacy or animal well-being”

• Action: Charity Entrepreneurship incubator

• Open Philanthropy advice for grant-seekers

• Open phil funding sheet

• Longview grantmaking

• 80k hours job board

---

Agendas and research questions

• GPI research agenda

• Open Philanthropy “Technical and Philosophical Questions That Might Affect Our Grantmaking”

• Happier lives institute research priorities

• Effective thesis project

Message

We can now do better than this vague hope!

The founders of Effective Altruism took ideas from Philosophy, Economics, and other parts of academia to build a rigorous approach to ‘doing the most good in the world’, and to exploring and measuring this.

• Miraculously, “EA happened”

Miraculously, EA also has a passionate and influential group of supporters, and a substantial pool of funds for research, interventions, and advocacy!

---

Big opportunity for academic researchers to have impact, inspiration, funding

• Partnering within academia, EA research audience

• Grants

• Helping students

• Leave academia for greener (?) pastures at an EA-aligned org

< EA and global priorities research offers a huge opportunity for academic researchers to have an positive impact (on the allocation of funds, and on the market of ideas).

There are opportunities for funding to support your research within academia, to promote the impact of your research (and gain valuable feedback), to help students find meaningful careers/research

…and/or you may want to leave academia work directly for an EA-aligned organization (like I did).

Things I’m not going to cover:

• “Things I’m not going to cover” (no time)

What/who is EA? What research/funding is there?

What is EA?

Doing the ‘most good’ given limited resources (some relationship to utilitarianism)…

but how do we define ‘the most good’?

---

What do EA’s prioritize most?

1. Global Poverty
2. Artificial Intelligence Risk
3. Climate Change
4. Cause prioritization
5. Animal welfare

---

Results of impromptu survey at LIS conference: What they think EA’s prioritize

.kable_styling <- hijack(kableExtra::kable_styling, full_width=FALSE)
.kable <- hijack(knitr::kable, format.args = list(big.mark = ",", scientific = FALSE))

lis_ea_priority_guess <- factor(c(
"Cause prioritization",
"Global Poverty",
"Cause prioritization",
"Climate Change",
"Global Poverty",
"Climate Change",
"Global Poverty",
"Global Poverty",
"Cause prioritization",
"Cause prioritization",
"Climate Change",
"Cause prioritization",
"Climate Change",
"Artificial Intelligence Risk",
"Climate Change",
"Global Poverty",
"Cause prioritization",
"Cause prioritization",
"Climate Change",
"Global Poverty",
"Cause prioritization",
"Cause prioritization",
"Cause prioritization",
"Global Poverty",
"Global Poverty",
"Global Poverty",
"Cause prioritization",
"Climate Change",
"Climate Change",
"Global Poverty",
"Global Poverty",
"Climate Change",
"Global Poverty",
"Global Poverty",
"Cause prioritization",
"Climate Change",
"Global Poverty",
"Global Poverty"
)
) 




lis_ea_priority_guess <- tibble(
  lis_ea_priority_guess = lis_ea_priority_guess
)

lis_ea_priority_guess %>% tabg(lis_ea_priority_guess) %>% 
  .kable(caption = "What LIS resspondents *think* EAs prioritize most:" ) %>% 
  .kable_styling()

What LIS resspondents think EAs prioritize most:

lis_ea_priority_guess	n	percent
Global Poverty	15	0.39
Cause prioritization	12	0.32
Climate Change	10	0.26
Artificial Intelligence Risk	1	0.03

---

2020 EA survey: all responses

(
priority_ordered_bar <- lik_priority_eas20 %>%
      as.data.frame() %>%
    select(-engagement_num) %>%
    likert() %>%
  plot(.,
     type="bar") +
  ggtitle(title)
)

All responses

---

… ‘Highly engaged’ (self-rated)

(
priority_ordered_bar_eng <- lik_priority_eas20 %>%
  filter(engagement_num==5) %>%
        select(-engagement_num) %>%
   as.data.frame() %>%
    likert() %>%
  plot(type="bar") +
  ggtitle(title)
)

Highly engaged only (rated 5/5)

---

How much effective altruism/global priorities research funding is there?

How much effective altruism/global priorities research funding is there?

---

(
op_research_grants_tab <-
open_phil_grants %>%
  filter(possible_research==TRUE) %>%
  group_by(year) %>%
  dplyr::summarise(total =  format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
  arrange(-year) %>%
    mutate(year=as.character(year)) %>%
    .kable(caption = "Open Phil (likely) research funding by year")  %>%
 .kable_styling()
)

Open Phil (likely) research funding by year

year	total	grants
2021	19,379,223	13
2020	77,681,029	76
2019	122,463,958	63
2018	38,589,084	52
2017	57,666,403	45
2016	22,386,936	27
2015	2,591,000	9
2014	1,437,720	3
2013	445,000	2

---

By year and focus area:

(
  op_res_grants_tab_yr_area <-
open_phil_grants %>%
    filter(possible_research==TRUE) %>%
    dplyr::group_by(year, focus_area) %>% # drop_na(!!yvar, !!treatvar) %>%
    summarise(total = sum(amount_usd_k,  na.rm = TRUE)) %>%
    spread(year, total, fill=0) %>%
    arrange(-`2020`) %>%
    .kable(caption = "OpenPhil (likely research) grants by year and area, in $1000 USD") %>%
    .kable_styling()
)

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

OpenPhil (likely research) grants by year and area, in $1000 USD

focus_area	2013	2014	2015	2016	2017	2018	2019	2020	2021
Scient. Res.	0	0	0	7,085	27,500	19,720	43,770	43,751	672
Biosec.	0	0	300	1,943	7,747	8,704	1,625	12,792	1,000
AI risk	0	0	1,186	6,333	10,798	3,128	61,288	10,891	15,450
Glob. Catastr.	0	0	0	3,070	3,758	0	1,703	4,586	1,500
Farm Animal	0	0	0	820	2,022	4,580	3,296	3,261	756
Other	0	0	10	500	1,550	47	1,050	806	0
Glob. Health/Dev.	0	0	0	0	2,864	210	3,176	678	0
Macro-econ	0	0	0	700	0	700	1,150	600	0
Immig. Pol.	0	1,185	390	0	0	400	0	200	0
Crime/Justice	445	0	180	1,636	1,427	1,101	5,066	115	0
Land Ref.	0	0	275	300	0	0	340	0	0
US pol.	0	253	250	0	0	0	0	0	0

(
  op_res_grants_line <-
open_phil_grants %>%
  group_by(year, focus_area) %>%
    mutate(total = sum(amount_usd_k, na.rm = TRUE)) %>%
    ggplot() +
    aes(x = year, y = amount_usd_k, colour = focus_area) +
    geom_jitter(width = 0.5, height = 0.2, size=0.8) +
  scale_colour_discrete(labels = function(x) str_wrap(x, width = 15)) +
    geom_line(aes(x=year, y=total)) +
  ylab("Grant amounts in $1k")
)

(
open_phil_grants %>%
  filter(possible_research==TRUE) %>%
  filter(year==2020) %>%
  group_by(Focus.Area) %>%
  summarise(total = format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
  dplyr::arrange(-grants) %>%
  .kable(caption = "Open Phil (likely) research funding, 2020") %>%
  .kable_styling()
)

Open Phil (likely) research funding, 2020

Focus.Area	total	grants
Scientific Research	43,750,718	33
Farm Animal Welfare	3,261,351	13
Potential Risks from Advanced Artificial Intelligence	10,891,345	8
Biosecurity and Pandemic Preparedness	12,792,330	7
Global Health & Development	678,358	5
Criminal Justice Reform	115,000	3
Global Catastrophic Risks	4,586,224	2
Macroeconomic Stabilization Policy	600,000	2
Other areas	805,703	2
Immigration Policy	200,000	1

---

By organization

(
  op_res_grants_tab_orgs_area <-
open_phil_grants %>%
    filter(possible_research==TRUE) %>%
    dplyr::group_by(Organization.Name) %>% # drop_na(!!yvar, !!treatvar) %>%
  summarise(total = sum(amount_usd_k,  na.rm = TRUE), `number of grants` = n()) %>%
      arrange(-total) %>%
    filter(total>5000) %>%
    .kable(caption = "OpenPhil (likely research) grants by corganization  and area, in $1000 USD") %>%
  .kable_styling()
)

OpenPhil (likely research) grants by corganization and area, in $1000 USD

Organization.Name	total	number of grants
Georgetown University	55,250	2
UC Berkeley	29,552	18
Nuclear Threat Initiative	20,439	6
Sherlock Biosciences	17,500	1
Machine Intelligence Research Institute	14,756	5
University of Washington (Institute for Protein Design)	11,368	1
Stanford University	8,294	1
Open Phil AI Fellowship	6,760	4
Arizona State University	6,421	1
University of Southern California	6,238	3
Rutgers University	5,982	2
MIT Synthetic Neurobiology Group	5,970	2
Stanford University	5,752	10
Telethon Kids Institute	5,300	1
Foundation for Food and Agriculture Research	5,292	6
Harvard University	5,068	3

Other organizations funding or doing EA/GP research, or likely interested

knitr::include_url("https://www.longview.org/grantmaking",  height = "800px")

EA funding model

Maybe 500 million USD per year in EA/adjacent donations (+ about 250 million from OpenPhil)

• GiveWell moving ~$80M per year

• Founders Pledge

• Longview

• Effective Giving

• EA Funds

• “Gates Foundation seems to do ~$100M-500M/yr of grants in global economic development that seem to have cost-effectiveness on par with GiveWell work”

It’s not all research funding, but some of it is, and it is all interested in prioritization/effectiveness research.

There is a large amount of Lots from tech and crypto billionaires/millionaires. Some ‘bigger fish’ on the line too like Elon Musk –>

Other research orgs

knitr::include_url("https://eaforum.issarice.com/posts/f6kg8T2Lp6rDqxWwG/list-of-ea-related-organisations", height = "800px")

Research orgs (partial list):

• Future of Humanity Institute (about 70 including affiliates, fellows, affiliates)
• Global Priorities Institute (about 20 researchers/affiliates)
• Rethink Priorities (11 researchers, hiring more)
• GiveWell (about 10 researchers/advisors)
• Animal Charity Evaluators (5-ish)
• Machine Intelligence Research Institute (about 20)

How do most ‘effective altruists’ engage; what is most strongly advocated?

Quiz to audience?

1. Political action
2. Effective charitable donations/Earning to give
3. Pursuing a high-impact career
4. Avoiding environmental damage through personal actions

lis_engage_priority <- factor(c(
"Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
"Pursuing a high-impact career",
 "Pursuing a high-impact career",
 "Pursuing a high-impact career",
 "Pursuing a high-impact career",
"Pursuing a high-impact career",
"Pursuing a high-impact career",
"Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Effective charitable donations/Earning to give",
 "Pursuing a high-impact career",
"Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Pursuing a high-impact career",
 "Pursuing a high-impact career",
"Effective charitable donations/Earning to give",
"Pursuing a high-impact career",
"Pursuing a high-impact career",
"Pursuing a high-impact career"
), 
levels = c("Pursuing a high-impact career", "Effective charitable donations/Earning to give", "Avoiding environmental damage through personal actions", "Political action")
)


lis_engage_priority <- tibble(
  lis_engage_priority = lis_engage_priority
)

lis_engage_priority %>% tabg(lis_engage_priority) %>% 
  .kable(caption = "What LIS respondents *think* most 'effective altruists' engage... is most strongly advocated?:" ) %>% 
  .kable_styling()

What LIS respondents think most ‘effective altruists’ engage… is most strongly advocated?:

lis_engage_priority	n	percent
Pursuing a high-impact career	15	0.65
Effective charitable donations/Earning to give	8	0.35
Avoiding environmental damage through personal actions	0	0.00
Political action	0	0.00

From EA Surveys, EA orgs

2019 survey: career plans

• EAs are moving towards pursuing impact through their careers.

• 80k hours statements

Since 2015 “80,000 Hours thinks that only a small proportion of people should earn to give long term” (MacAskill)

There is also some support for politial influence, at least they say “the hour you spend voting is likely to be the most impactful one in your entire year on average… …influence over how hundreds of thousands or millions of dollars are spent.”

---

EA Careers (survey)

Careers: high vs less engaged

Source - EA survey 2020 forum post

My story

Reinstein CV

Why am I telling you my story?

Telling you my story because it might help you understand the strengths and limitations of academia and working at an EA org, and whether this aligns with your interests.

From my web CV …

---

---

Berkeley:

• Proposing ‘new ideas’ like the Poor Card

Limited audience…

‘How does this inform government policy?’, ‘How does it inform/relate to standard Economics (tractable mathematical) models of optimization?’, ‘Will this publish well’?

---

• ‘Does one donation come at the expense of another’?

• Should an ‘efficient altruist’ purchase ‘fair trade’ products, bundling consumer choices with additional revenue to poor farmers/workers?
  

• Considering ideas with a pre-EA policy audience.

---

From Reinstein, 2011

---

Things I care about: lining up with concepts in the discipline? But I really cared about ideas and impact.

Essex, UK:

• Experiments/trials and observational work on charitable and gift-giving: social influences, types of income/uncertainty

• Applied microeconomic theory

---

Moved to Exeter, UK

• to pursue ‘Impact’ (ESRC grant, REF focus)

• Building teaching/research/outreach resources, such as

• innovationsinfundraising.org and ‘barriers to effective giving’

• “Researching and writing for Economics students”

---

• Positives: Support, research freedom, deep theoretical work

• Limitations: Politics/management, journal games, less-engaged students

• Limitations

Positives: A fairly supportive environment, research freedom, many great colleagues, moderate teaching, targets ‘deep and rigorous theoretical work’, some of the smartest people

Limitations: academic politics and poor upper-management, countervailing rewards system, constant discussion of points/games (value drift), students

Standard publications as the only way to prove value; limits collaborative and nonstandard work

UK academia rewards either ‘REF-points publications’, box-ticking accreditations, or currying favor with ‘admin’

Rethinking my priorities (eyeroll)

In 2021 I left my secure academic post:

• To pursue greater impact …

• To build tools and programs…

To pursue greater impact as a researcher at Rethink Priorities, a think tank “dedicated to figuring out the best ways to make the world a better place.” RP is closely tied to the Effective Altruism movement. My research into effective charitable giving is made possible by a grant from an individual donor under the advising of Longview Philanthropy.

To build tools and programs promoting open, collaborative, and robust research, as well as teaching, learning, and research training outside of traditional university degree schemes.

(Should you do it too? Back to this at the end)

What I’m doing now

0. Longview-funded: ‘Barriers to effective giving’

• Categorization and links to Economics and Psychology ‘theory’

• Meta-analysis and synthesis

• Field experiments and trials in large-scale contexts

---

1. EA Survey and ‘movement building’

• Survey methods: representativeness, survey design

• Identifying key questions for ‘tracking a movement and its impact’, e.g.,

  • “Which people are likely to contribute the most; ‘selecting’ where to promote’” (ML and inference)
  • “What will the impact of the EA community be in 10-20 years?” (APC modeling)
  • Do anticipated donations tend to exceed actual donations and when/why

• Analysis: Visualisation, descriptive, predictive, and

…

---

2. Outreach and message testing

• Receptiveness to parts of EA message

• Support for policies (e.g., animal welfare)

3. Future work: moral weights, measurement of satisfaction, ‘near-term’ (global health) evaluation, ‘shallow reviews’

4. Support and guidance to other RP projects (e.g., modeling the meat industry; designing behaioural trials)

5. Connection with academia; publishing, recruiting, advising students

Effective altruism and global priorities

What are the ‘important cause areas’?

Near term, Long-term future, Animal welfare, Prioritization research and ethics

Near-term

• Global health, environmental
• Extreme poverty
• Mental health
• Manmade/political suffering
• Global warming

Long-term

• Existential risk
• ‘S-risk’ (suffering)
• Population growth/expansion
• Global catastrophic risks: Artificial Intelligence ‘alignment’, Bio risks and epidemics, Nuclear/political

Animal welfare

Land animals in factory farms, plot over time

• Factory farming and fishing
• Wild animal welfare

Land animals in factory farms, plot over time

---

---

E.g.,

• How to create better systems for creating and disseminating knowledge

• Whether and how to ‘discount’ future individuals (income or happiness)?

• How to make choices under moral uncertainty

• How to value ‘more happy people’ versus ‘happier people’ (population ethics)

---

EA perspectives: Why do we need research, what should research do?

1. Defining a moral, value and choice framework, working out thorny moral decision-optimization issues

2. Empirical measurement of value and ‘what works to achieve it’

3. Empirical evidence on persuasion: ‘how to get people to act pro-socially and effectively’

---

Defining a moral framework, considering ‘what has value’, and ‘how to learn and choose’?

… how to value things and be consistent, how to use uncertainty and information in making altruistic choices

E.g.,

• moral weights

• population ethics

• moral uncertainty .

---

From GPI agenda:

Under what conditions would a social planner or philanthropist prioritise policies that primarily increase social welfare in the far future rather than in the near term? For instance, under what condition would such agents prioritise saving for future generations (Ramsey 1928) or reducing the risk of human extinction (Baranzini and Bourguignon 1995)?

Should one have the same levels of epistemic modesty about unusual moral views as one should about unusual empirical views?

To what extent should we be risk averse in our approach to doing good, and what are the implica/tions of reasonable risk aversion for global prioritisation? (Quiggin 1982…)

Social welfare criteria that are used to compare states that differ in population size typically specify a critical welfare level at which lives that are added to the population have zero contributive value to social welfare (Blackorby et al. 1995…). What kinds of lives have zero contributive value in this sense (Cockburn et al. 2014…)?

---

Empirical measurement informs ‘production function for good’

• ‘What has value and moral worth?’ (e.g., sentience research)

• How to measure value? (e.g., pain/pleasure DALY)

• How to achieve value?

• Direct interventions and policies, direct/indirect, short and LT impacts

• Very long term impacts, inference with deep uncertainties

Should we loosen migration restrictions to increase global welfare? What is a politically feasible level of migration? (from Rhys-Bernard syllabus)

Estimating, in terms of SWB, the impact of potentially highly-effective interventions, including: psychotherapy for common mental disorders; cataract surgery for blindness; deworming tablets to improve lifelong earnings (from Happier lives institute research priorities)

---

Empirical (behavioural) … How to get people and institutions to care about others (altruism) and about being effective in doing so?**

?

---

• Barriers to considering effectiveness and acting effectively (my focus – open project HERE)

• Applied work : message testing, information and choice-architecture

Open Philanthropy

“Technical and Philosophical Questions That Might Affect Our Grantmaking”

See also: this [list of lists(https://forum.effectivealtruism.org/posts/MsNpJBzv5YhdfNHc9/a-central-directory-for-open-research-questions)]

How research in EA differs from research in academia (motivations, how success is measured)

Academia vs EA (Rethink Priorities)

Academia:

• Publications, grants, citations, students placed in jobs, awards

• You need to be ‘first to publish’ on a new topic; supporting evidence less valued

RP:

• Impact on funds allocated and ‘influence’ on decisionmakers
• Publications and grants as a means to an end; discussion of how much to publish traditionally versus other outlets

model estimates impact by considering the probability of our influence targets updating in the correct direction … amount of money and resources that are changed, how much better (or worse) that change is, the counterfactual years of credit due to the work, and the costs of the project.

Operationalizing: ‘Which research to prioritize’?

’How to measure research impact?

Explained:

Money-metric impact of a random-ish sample of projects:

‘Multiplying the uncertain estimates of’ (Bayesian):

• Probability our research changes the “influence-target’s” choice \(\times\)
• probability this is in the right direction \(\times\)
• Funds affected \(\times\)
• Proportional increase in the funds’ impact \(\times\)
• “Counterfactual years impacted”

Divide by project cost

\(\rightarrow\) “Impact per dollar spent”

\(\rightarrow\) Extrapolate to total value of RP

Changing the game: Ideas for more productive system of peer feedback and credibility

The EA research landscape - a new ‘funder’ and consumer of research

We care about research value, accuracy, and impact, not as mch ‘innovation’ or theoretical rigor

We are not so tied to traditional institutions (publishers, traditional grantmakers)

---

\(\rightarrow\) ‘Evaluated project repo’ rather than frozen publications?

Can we help academia escape the journal publication collective-action problem?

How you can get involved; opportunities

FHI

Rethink Priorities

Rethink Priorities is hiring – “Staff Researcher (Global Health and Development)”

• several positions

• and very soon “a longtermist and a meta/movement building person”

GPI, Grants, Action

• GPI/Forethought fellowship, [pre-doctoral](https://globalprioritiesinstitute.org/wp-content/uploads/GPI-Predoctoral-Research-Fellow-Economics.pdf

• EA Funds grants, e.g,, for “Promising research into animal advocacy or animal well-being”

• Action: Charity Entrepreneurship incubator

• Open Philanthropy grants

OP: ramping up, very interested in funding research but “In general, we expect to identify most giving opportunities via proactive searching and networking”

How can academics support and do EA/GP research within universities

• Contact EA organisations; present your work

• Help build open-content

---

Encourage your students

• to apply for GPI/FHI/RP fellowships and internships
• to engage with the Effective Thesis Project

Who should want to work at an EA org like RP? Why or why not?

Note: RP and GPI differ dramatically in focus and approach; I’m mainly talking about RP.

YES if you (because you like)…

• Care about impact and social good; research outcomes, implications, applications

• Rigorous analytical framework (Philosophy, Economics, Statistics/data) anchored and connected to the practical

• “Interdisciplinary” in the right way

• Positive, supportive environment: people motivated by outcomes, zero competitiveness/politics AFAIK

• High-achieving, super-literate and ‘switched on’ colleagues

---

NO if you want to…

• Focus on deeply theoretical ‘pure’ research without a direct connection to impact, a completely independent research agenda

• Earn a lot of money, have the long-term job security of ‘tenure’

MAYBE if you want to…

• Teach/work with a large group of motivated young people, guide careers

• Publish in academic journals and the equivalent, present at academic conferences

• Prestige/public intellectual

Which fields?

Economics: Both theory and empirical/application, development economics, behavioral, preference and choice theory, macrroeconomics, and more

Philosophy: The answers to ‘arcane’ philosophical questions are now driving very important decisions and uses of funds

Psychology and behavioral science: “How to get people to care”, “how to do messaging and how to measure the results”

---

Maths, statistics, computer science: AI-risk, data science, mathematics of uncertainty and forecasting, controlling technology, statistical experimental design…

Political science and international relations/area studies

Biology and neuroscience (RP just hired an entymologist): Wild animal welfare, animal sentience

Other sciences: Existential risks (natural and human)

---

• EA tries to be analytically rigorous and to make decisions based on empirical evidence

• There is a lot of money your research can move

• There is money to get the research done (e.g., FHI, Open Phil, CEA, Center for Long Term risk…)

• A diverse set of relevant research areas, …

  • ‘defining the neutral point of subjective well being’ of interes
  • Animal science
  • QALY and DALY definition and measurement
  • AI Safetey
  • Giving and messaging
  • Much more.